Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students

TL;DR

This paper introduces a new method to accurately identify peer effects on effort in networks, accounting for unobserved shocks and isolated students, leading to more precise estimates than traditional GPA-based approaches.

Contribution

The authors develop a novel framework that separates unobserved shocks from effort-influencing shocks, improving peer effect estimation in network data with isolated students.

Findings

Peer effect estimates using GPA as a proxy are 40% lower with the new approach.

Classical estimates can significantly differ when the network includes isolated students.

The new method provides more accurate peer effect measurements in educational settings.

Abstract

Peer influence on effort devoted to some activity is often studied when effort is unobserved, and the researcher instead observes an outcome that combines effort with other shocks. For instance, in education, achievement measures such as GPA reflect both effort and idiosyncratic GPA shocks. We propose an alternative approach that circumvents this approximation. Our framework distinguishes unobserved shocks to GPA that do not affect effort from preference shocks that do affect effort levels. We show that peer effects estimates obtained using our approach can differ significantly from classical estimates (where effort is approximated) if the network includes isolated students. Applying our approach to data on high school students in the United States, we find that peer effect estimates relying on GPA as a proxy for effort are 40% lower than those obtained using our approach.